Research on stroke outcomes has focused almost exclusively on recovery of very basic functions, such as feeding oneself, bathing, walking, and speaking. However, impairments in social function, including impaired recognition and expression of emotions, are also common consequences of stroke. Diminished affective prosody (understanding and conveying emotion through vocal intonation, rate, pauses, and stress), is a particularly common impairment after right hemisphere stroke, and can be misinterpreted as loss of empathy, apathy, or depression (problems that can also be important consequences of stroke, but require different management). Appropriate expression and recognition of prosody is also critical for effective social interaction. Despite the impact of diminished affective prosody on quality of life and function in society, these disorders are understudied, and there are few evidence-based treatments for these disorders. To design effective and efficient interventions that will improve quality of life and facilitate full participation in society, and to plan treatment trials to evaluate the interventions, we first need to lay the groundwork. We need to identify the perceptual, cognitive, and motor deficits that can disrupt affective prosody; characterize the natural history of recovery and variables that influence recovery of these deficits; and identify the neural networks that support these functions and their recovery. Identifying the networks that support these processes will allow us to select interventions that will help recruit these networks to augment behavioral therapy. In this project we will integrate: 1) detailed longitudinal analysis of the impaired perceptual, cognitive, and motor processes underlying prosody in each patient at four time points over the first year after stroke, and (2) detailed longitudinal analysis of the structural and functional lesions (e.g., in right ventral and dorsal streams) and functional connectivity between critical regions in each patient at the same four time points over the first year after stroke, and (3) analyses of variables (such as timing and doses of antidepressants) and co-morbidities (such as depression) that influence recovery. By integrating these methods, we will have a better understanding of the natural recovery trajectories and the neural basis of both the impairments and their recovery. Combining these datasets will allow us to test specific hypotheses about the perceptual, cognitive, and motor processes and their neural mechanisms underlying affective prosody and about recovery of these process after disruption due to focal lesions. Furthermore, by building linear mixed effect models to identify the potential impact of independent variables on outcomes, we will be able to prognosticate and identify variables that modify prognosis. These three sets of data will also provide a foundation for designing treatments that combine behavioral therapy with medications or neurally-targeted interventions such as transcranial direct current stimulation. Integration of these datasets will also provide evidence to guide who needs treatment, when to treat, and where in the brain to treat these functions.